The main objective of this study is to develop and evaluate an effective integrated learning approach for the automatic classification of Municipal Solid Waste using the TrashBox dataset, comprising 17,785 images, to ...
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The main objective of this study is to develop and evaluate an effective integrated learning approach for the automatic classification of Municipal Solid Waste using the TrashBox dataset, comprising 17,785 images, to improve the sorting of recyclable waste materials, reduce landfill usage, and promote sustainable environmental practices. Initially, four deep learning models-DenseNet161, ResNet152, and MobileNetV3 variants-are explored to determine the most suitable feature extraction method. During the featureselection phase, three metaheuristic algorithms-Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawk Optimization (HHO)-are applied to filter out irrelevant features and retain significant ones. These selected features are then fed into machine learning classifiers-Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbor (KNN)-for final predictions. The DenseNet161-HHO-SVM combination outperforms other models in this study, achieving the highest accuracy and lowest execution time. This integrated approach also demonstrates superior performance (97.45%) compared to previous state-of-the-art models on the same dataset, with the data processing and method integration phases having substantial impacts.
The accuracy of land crop maps obtained from satellite images depends on the type of featureselection algorithm and classifier. Each of these algorithms have different efficiency in different conditions;therefore, de...
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The accuracy of land crop maps obtained from satellite images depends on the type of featureselection algorithm and classifier. Each of these algorithms have different efficiency in different conditions;therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal featureselection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features' capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.
This paper introduces a novel clustering approach based on Minkowski's mathematical similarity to improve EEG featureselection for classification and have efficient Particle Swarm Optimization (PSO) in the contex...
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